prompt_jev() now works on images

Typed Graphs, Checked Types: Graph as Code for Agentic Engineering and Data Quality

Agents write faster than humans can review. Typed graphs check every write, checked queries validate every question, and branches keep the human where true is decided.

Red Flags to Watch for When Choosing Between AI Pentesting Vendors

Choosing between AI pentesting vendors requires looking past marketing labels like 'autonomous' or 'agentic' because the underlying systems vary widely. A Stanford-led study ran different AI agent frameworks against a live 8,000-host network and found that between 55% and 82% of reported findings were confirmed as real vulnerabilities. Swapping only the underlying model moved one framework's valid finding rate from 57% to 80%, yet that same framework still scored lowest overall since its findings stayed shallow. On a CTF-style benchmark two frameworks scored near-identically (48.6% vs 45.9%), but on the live network one scored nearly three times higher on severity and complexity. Buyers should demand proof of exploitation, real-network results, clear safety controls, data handling answers and clarity on what 'continuous' actually means.

API pricing vs subscription: what my month of Codex would cost on the API

I priced 30 days of my real Codex usage at API rates. A $100 plan covered $745 of API-equivalent work. Here's the math, the script to run on your own logs, and when the API still wins.

RAG vs. Vectorless RAG, clearly explained!

...explained visually (with tradeoffs in production).

DeepSeek Super-Agent Architecture Explained

How DeepSeek Harness's Super-agent works: a Router, isolated Workers, event triggers and a Watchdog let one AI agent run many tasks. Diagrams inside.

Giving the Optimizer a Hint: What’s New in MariaDB Server 13.0, compatibility and beyond

Discover optimizer hints in MariaDB Server 13.0: MySQL-compatible syntax, query plan control, and advanced features for views and query blocks.

How I use AI (Oct 2026)

I document how I use AI, one year after my first writeup. Everything's completely different.

AI in Production Without a WHERE Clause

Bối cảnh triển khai AI mà không có bước review rủi ro giống như thiếu một WHERE clause trong SQL có thể dẫn đến đêm dài khắc phục sự cố. Nguyên nhân kỹ thuật nằm ở việc các model AI có thể tạo ra dữ liệu không mong muốn khi không có bộ lọc phù hợp. Hệ quả có thể rất nghiêm trọng khi hệ thống AI vô tình tạo ra nội dung độc hại, sai lệch hoặc vi phạm chính sách. Đáng học hỏi là bài nhấn mạnh việc xác định rõ vị trí của cơ chế "phanh" (brake) trong hệ thống AI và ai chịu trách nhiệm kiểm soát nó.

Bài viết này giải thích tại sao thiếu bước kiểm duyệt trong AI sản xuất có thể dẫn đến hậu quả nghiêm trọng như lỗi query SQL không có WHERE clause.